I present an automated method for computing the spatial similarity of tumor location with respect to organs at risk between multiple head and neck cancer patients and ranking the patients accordingly. I use this method and resulting metric to predict, for each patient, a radiation dose distribution across organs in the head and neck. Furthermore, I go over the design and implementation of a visual analysis tool used to help validate the method. 
The method uses as input DICOM (Digital Imaging and Communications in Medicine) files provided by our collaborators at MD Anderson Cancer Center. We preprocess these files to extract primary data components and translate them into an efficient format for the automated method and web-based application to read in. The method is written in Python, and utilizes the Structural SIMilarity (SSIM) algorithm for computing similarities. A JSON (JavaScript Object Notation) file is generated containing all patients, and for every patient a dose prediction and ordered list of most similar patients is produced. The web-based GUI (Graphical User Interface) reads in the JSON file and lets users visually explore the data, analyze the method, and gather insights for possible radiation therapy plans. 
The resulting method and interface, CAMP-RT (Correlations Across Multiple Patients in Radiation Therapy) is continuously being evaluated by domain experts from the MD Anderson Cancer Center. I report their qualitative feedback regarding the method and interface. I further evaluate quantitatively the results of the prediction using a dataset of 101 head-and-neck cancer patients. The results indicate the method has good predictive capabilities.

Main View of Application

CAMP - RT: A Patient Similarity-Based Method for Predicting Radiation Dosage in Head and Neck Radiation Oncology

Thanks to technological advancement and lower priced head-mounted displays, Virtual Reality is becoming popular in the consumer market. One of the main aspects on which researchers are focusing on these days, besides increasing the resolution of the displays, is finding a way to interact in a way that is as similar as possible to the real world. In this document, we will first analyze some previous work done in the field. Then, we will show our implementation of a passive feedback system which uses real objects to augment virtual reality experiences. The system will be used on two different sets of real objects. The first is composed of the exact replica of the objects present in the virtual environment, the second is composed of objects with several levels of mismatched physical characteristics. The document will then explain in detail the integration of the system in VR applications using Unity. 
In the second part of this document, we will describe a user study to compare the execution of object manipulation tasks with the system that we developed to two other state-of-art input devices: Leap Motion and VR Controllers. We will analyze the factors of the task execution in virtual reality, such as the accuracy, velocity, time to execution, level of realism and immersion, and we will compare the results with the execution of the same task in the real world. Finally, we will give suggestions on how to enhance our system in future work.

User wearing trackers on the hands for object manipulation tasks.

Passive Haptic Feedback for Object Manipulation in Virtual Reality

Large format displays&rsquo; popularity is constantly increasing in universities and workplaces due to their prices showing a constant decrease over time. Many studies have demonstrated their benefits on several kind of tasks, from navigation of 3D environments to complicated tasks that require high cognitive load, and these studies have demonstrated how they can increase productivity and the quality of work in multi-user collaborative environments. 
I am studying the general problem of using large format displays in a multi-user collaborative environments with the specific problem of university study course planning. The traditional approach is represented by a regular whiteboard that will be compared with a project planning/scheduling software developed for large format displays. 
The purpose of this study is not only to confirm previous studies&rsquo; results that claim that the usage of large format displays in a multi-user collaborative environment can increase productivity and work quality, but also to evaluate and understand how their usage influences users&rsquo; cooperation and collaboration while performing a complex collaborative task. We will study how the subjects actually interact, and how different is their interaction, both with tools and other subjects, compared to the traditional approach. 
I developed an application for project planning that runs on SAGE2, an open-source middleware offering the possibility to exploit a large format display of any resolution and size. It also allows multiple users to control it at the same time, connecting to it with a regular web browser interface and a laptop, working in a shared environment. 
I will show four simple techniques to implement a general project planning application for large displays and I will show four different possible realizations using the same techniques, with a detailed overview on the one created for the user study. 
An innovative approach to collect data from the user study will be shown and explained in detail. Such approach improves and automatize the qualitative data analysis that must be conducted in similar studies. It includes space tracking of participants and automatic coding. 
Results show that the developed application is not missing any critical feature and it could be a good substitute of the traditional approach. Data suggests that collaboration behaviors could be influenced by such technology, and some slight differences on collaboration were found, but not enough to constitute statistical evidence. In a similar way, no statistical evidence was found about improvements in performance over classic whiteboard technology.

The workflow of a general interaction.

Collaborative Project Planning Using Large Format Displays

Drug-drug interaction (DDI) alerts safeguard patient safety during medication prescribing, but are often ignored by physicians. Despite attempts to improve the usability of such alerts, physicians still mistrust the relevance of simplistic computerized warnings to support complex medical decisions. By building on prior fieldwork, this paper evaluates novel designs of trust - eliciting cues in DDI alerts. A sequential mixed-method study with 70 physicians examined what trust cues improve compliance, promote reflection, and trigger appropriate actions. In a survey, 52 physicians rated the likelihood of compliance and usefulness of redesigned alerts. Based on these findings, alerts were assessed in a scenario-based simulation with 18 physicians prescribing medications in 6 patient scenarios. Our results show that alerts embodying expert endorsement, awareness of prior actions, and peer advice were less likely to be overridden than current alerts, and promoted reflection, monitoring, or order modifications - thus building towards greater attention to patient safety. 
Keywords: healthcare information systems; health informatics; clinical alerts; trust in socio-technical systems; interaction design; decision-making 
<ahref="https://doi.org/10.1093/iwc/iwx020">doi:10.1093/iwc/iwx020</a>

DDI alerts were displayed in an experimental setting that simulated an inpatient meeting room where physicians use a workstation to prescribe medications. Interruptions were simulated using a second computer and screen interactions recorded using Camtasia

Design and Evaluation of Trust - Eliciting Cues in Drug - Drug Interaction Alerts

Purpose 
To evaluate the effect of transforming a right-censored outcome into binary, continuous, and censored-aware representations on radiomics feature selection and subsequent prediction of overall survival (OS) and relapse-free survival (RFS) of patients with oropharyngeal cancer. 
Methods and Materials 
Different feature selection techniques were applied using a binary outcome indicating event occurrence before median follow-up time, a continuous outcome using the Martingale residuals from a proportional hazards model, and the raw right-censored time-to-event outcome. Radiomic signatures combined with clinical variables were used for risk prediction. Three metrics for accuracy and calibration were used to evaluate eight feature selectors and six predictive models. 
Results 
Feature selection across 529 patients on more than 3,800 radiomic features resulted in increases ranging from 0.01 to 0.11 in C-index and area under the curve (AUC) scores compared with clinical features alone. The ensemble model yielded the best scores for AUC and C-index (often > 0.7) and calibration (Nam-D&rsquo;Agostino test statistic often < 15.5 with 8 df). The random forest feature selectors achieved the best performance considering all metrics. Random regression forest performed the best in OS prediction with the ensemble model (AUC, 0.75; C-index, 0.76; calibration, 8.7). Random survival forest performed the best in RFS prediction with the ensemble model (AUC, 0.71; C-index, 0.68; calibration, 19.1). 
Conclusion 
Including a radiomic signature results in better prediction than using only clinical data. Signatures generated randomly or without considering the outcome result in poor calibration scores. The random forest feature selectors for each of the three transformations typically selected the greatest number of features and produced the best predictions at acceptable calibration levels. In particular, random regression forest and random survival forest performed best for OS and RFS, respectively.

Pipeline for identification of radiomic signatures.

Evaluating the Effect of Right-Censored End Point Transformation for Radiomic Feature Selection of Data From Patients With Oropharyngeal Cancer

Umwelt, the German word for environment, is a psychology term referring to the individual&rsquo;s mental image of the surrounding world. This subjective universe is shaped by an organism&rsquo;s unique perception of the environment around them and, therefore, differs from individual to individual. In other words, organisms experience the world through their senses, construct the mental model of their vicinity, and react upon it. Hence, this image never remains static - instead it is constantly refined, as a result of the dynamic nature of the world. Umwelt is closely related to the concept of Situation Awareness, that is the perception of environmental elements, comprehension of their nature, and projection of their near future status with respect to time and/or space. 
Sensory perception (exteroception) of the surrounding environment is, as a matter of fact, restricted to the limitations of biological senses. For example, although we consider humans with five fully developed senses (sight, hearing, touch, smell and taste) as able-bodied individuals, they are essentially blind to a multitude of information that constantly surrounds them. To put things into perspective, both human sight and hearing, can only perceive a fraction of information- part of the electromagnetic spectrum (light waves), and of the sound frequencies accordingly. However, as sensory systems differ among species, other creatures have an entirely different perceptual experience, as they are able to perceive a distinct spectrum of information. 
Despite the fact that our limited human senses restrict our Umwelt and, therefore, our Situation Awareness, Human Augmentics, referring to technologies that expand the capabilities and characteristics of humans, allows us to peek into an invisible &dlquo;other&drquo; world. Thus, some important questions arise: What would the consequences of an expanded perception of the
surrounding environment be, and how would that affect an individual&rsquo;s Situation Awareness? Since our Umwelt is limited by genetics, how would individuals attune with characteristics and capacities that lie beyond human range? Could Human Augmentics possibly expand an individual&rsquo;s Umwelt, leading to improved Situation Awareness? 
Jakob von Uexkull (1864 - 1944), a German biologist who worked on animal behavior studies, cybernetics of life, and muscular physiology, is attributed for coining the term Umwelt. Uexkull&rsquo;s Umwelt is the phenomenal world of a living animal, in which it exists and acts, and as a result is shaped according to the stimuli received from its senses. Due to the fact that individuals, even across the same species, sense the world differently, each animal creates its own unique Umwelt. 
Perception and, as a result, interaction with the world begins with the raw input of data from the environment through the sensory system. Anatomically, the sensory system is part of the nervous system and includes the sensory receptors that act as probes of the environment, parts of the brain that are used for sensory perception, and the neural pathways that connect the brain with the receptors. Notably, humans&rsquo; sensory system consists of five traditional senses: Sight, hearing, taste, smell and touch. The stimulation type and bandwidth of each sensory channel are limited to its characteristics, which in turn constrain the size of the Umwelt.
However not all living organisms possess the same qualitative or quantitative senses. Depending on the environment in which they have evolved, their sensory system may be entirely different than ours; some have more while others have less sensitive senses. Others have quite different ones. Bats and dolphins, for instance, have the ability to calculate orientation and position of other animals and objects through reflected beams of sound, an ability known as echolocation. Sharks and rays, on the other hand, can detect changes in the electric field of their vicinity, an ability known as electroreception. 
Situation Awareness 
It is a well-known fact that humans are capable of sensing only a small fraction of the data that constantly surrounds us. Magnetic fields, infrared light, ultrasound, cosmic rays, radiation are just a few of the things that are completely invisible to us. However, recent technological advances in specialized sensors, enable the detection of some of this information. For instance, geiger counters are used by hazmat personnel to assess radiation levels in nuclear accidents. These technologies provide feedback using one of our existing senses, usually visual or audio, in order to help workers safely perform daily tasks. 
Situation Awareness (SA) is the perception of environmental elements, comprehension of their nature, and projection of their near future status with respect to time and/or space. In other words, it is the awareness of the immediate vicinity, comprehension of the current state of the environment and ability to predict what may happen. Inputs from the sensory system are used by the individual in order to create and update their Situation Awareness state, and as a result, decisions depend on the accuracy of the perceived model. Situation Awareness is tightly associated with experience; somebody less experienced may miss information that a more experienced user will not. To put things into perspective, the hazmat personnel&rsquo;s (that we discussed above) Situation Awareness includes the geiger counter readings, comprehension of their meaning and ability to predict what will happen if they raise above a certain level. 
Despite the fact that sensory systems have been honed by millions of years of evolution, they are very limited to a multitude of data. We are blind to a plethora of information that exists around us, and fortunately most of it is harmless. We depend on our sensory system to interpret the world, and we use our Situation Awareness to comprehend the environment around us and act upon it. As a result, lack of information for a certain situation may lead to misjudgements and accidents. In fact 76% of aviation errors, happen because the pilot did not perceive one piece of the airplane&rsquo;s crucial information. To that end, another important question arises: Given that today&rsquo;s technology can sense some of this invisible information, can we expand the Umwelt and improve Situation Awareness? 
Research Questions 
Genetic defects, diseases or accidents can lead to sense disabilities or complete sense lose, which has been observed to both animals and humans. To that end, individuals learn over time to use the remaining of their senses in order to overcome a disability. Recent scientific discoveries have shown that while this new ability results in anatomical changes of the brain, the responsible regions remain the same. In other words, even though the sensory input channel has changed, the brain will still process the information in the same region|a phenomenon known as Sensory Substitution. 
As a result, Sensory Substitution devices use one or more of the remaining senses to increase an individual&rsquo;s Situation Awareness through a different sensory pathway. More specifically, tactile displays are able to translate external environmental information onto one&rsquo;s skin, through the use of haptics. While these systems are primarily designed for people with disabilities, their concepts can be extended to other sensory deprived individuals, who find themselves in no or limited visibility environments due to external, environmental reasons. For example, firefighters operating within smoky, low visibility hazardous environments or soldiers fighting in pitch black darkness, are more likely to have no Situation Awareness of their surrounding environment. 
The ever-dynamic nature of the world around us, however, makes navigating with a wearable tactile display, a quite challenging task. From feeling the vibrations onto one&rsquo;s skin, comprehending their meaning and correctly acting upon them, to getting inundated with tactile sensory overload, tactile displays have great potential, when communicating information in an eloquent
way. 
In this dissertation we will explore different environment-to-vibration mappings while assessing one&rsquo;s Situation Awareness. Furthermore, we seek to understand whether one's experience with a tactile display would have an effect on their Situation Awareness. Finally, we hypothesize that a one-size-fits-all wearable tactile display would not perform equally well in all environmental scenarios and therefore we are going to explore how different vibration configurations, patterns and environment-to-vibration mappings would affect one&rsquo;s navigation. All of the above are distilled into the following research questions: 
- Could tactile displays be used to increase Situation Awareness of visually deprived individuals? 
- To what extent would Situation Awareness be associated with experience when using a tactile display?

Positioning of Sensor Modules and Controller Box

Supporting Navigation with a Torso Wearable Tactile Display

Pollution exposure assessment at the population level is an established enterprise for environmental scientists and public health officials - but efforts to help individuals monitor and track their personal pollution exposures have just begun to garner research interest. Self-tracking pollution exposure is challenging for several reasons, including current limitations in sensor size, accuracy, and cost, frequent calibration requirements, and that people&rsquo;s daily activities often interfere with data quality in wearable sensing. The goal of this research is to develop a human-centered computing framework for the emerging field of personal pollution exposure assessment. To that aim, in this position paper, we propose a Bayesian approach to combine environmental sensing data from different spatiotemporal resolutions, such as from citywide national monitoring stations, neighborhood-wide lightweight sensing nodes, and personal wearables. 
Author Keywords: Environmental sensing; pollution exposure profiling; self-tracking; pollution monitoring.

The phone application of myCityMeter, lookup (a), personal exposure profiles (b), and suggested actions (c).

Toward a Bayesian Approach for Self- Tracking Personal Pollution Exposures

Recent epidemiological studies suggest that age-related cognitive decline - particularly, the stages between normal cognitive changes in aging and early dementia - is adversely affected by environmental exposures, such as long-term air pollution and traffic noise. Although monitoring outdoor air pollution is now commonplace, and smart home solutions for monitoring indoor air quality is becoming prevalent, ways that the elderly can record long-term environmental exposures and adopt healthy lifestyle changes are little explored. We present myCityMeter, a pollution exposure management tool for older adults and their caregivers. myCityMeter measures the pollutants shown to be associated with cognitive impairment in older adults: PM2.5 and ambient noise. Using a set of neighborhood-level stationary and personal mobile sensors, myCityMeter helps users to monitor their environmental exposures, know potential exposures when planning activities, journal cognitive performances, and take day-to-day actions to avoid the environmental risk factors for early dementia. 
Author Keywords: Older adults; pollution exposure management; pollution monitoring; cognitive impairment; environmental sensing; pm2.5; noise; array of things.

An overview of myCityMeter, a personal pollution exposure monitoring tool that collects data from three monitoring sources.

myCityMeter: Helping Older Adults Manage the Environmental Risk Factors for Cognitive Impairment

Talkers intentionally producing high-intelligibility speech for listeners in challenging situations often reduce their speech rate. This study affords listeners fine-grained control over the playback rate of a desired speech signal in varying levels of background noise, and tests listener intelligibility with their preferred and unmodified rates of speech. We find clear listener preference for decreased rates of speech as background noise increased. However, we also found degraded performance on a speech-in-noise intelligibility test relative to unmodified speech in these same conditions. 
Index Terms: perception of prosody; adverse listening conditions; speech intelligibility

Score Difference, Expansion vs SNR

Effects of User Controlled Speech Rate on Intelligibility in Noisy Environments

Background 
The aim of this study was to determine the interdisciplinary agreement in identifying the post-operative tumor bed. 
Methods 
Three radiation oncologists (ROs), four surgeons, and three radiologists segmented post-operative tumor and nodal beds for three patients with oral cavity cancer. Specialty cohort composite contours were created by STAPLE algorithm implementation results for interspecialty comparison. Dice similarity coefficient and Hausdorff distance were utilized to compare spatial differentials between specialties. 
Results 
There were significant differences between disciplines in target delineation. There was unacceptable variation in Dice similarity coefficient for each observer and discipline when compared to the STAPLE contours. Within surgery and radiology disciplines, there was good consistency in volumes. ROs and radiologists have similar Dice similarity coefficient scores compared to surgeons. 
Conclusion 
There were significant interdisciplinary differences in perceptions of tissue-at-risk. Better communication and explicit description of at-risk areas between disciplines is required to ensure high-risk areas are adequately targeted. 
Keywords: Oral cavity cancer, Target delineation, Post-operative, Interdisciplinary, Interobserver

Illustration of the overlap metric where A represents the observer and G represents the gold standard (STAPLE).

A Prospective In Silico Analysis of Interdisciplinary and Interobserver Spatial Variability in Post-Operative Target Delineation of High-Risk Oral Cavity Cancers: Does Physician Specialty Matter?

We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient&rsquo;s probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration. 
Index Terms - Visual analytics, precision medicine, design studies, nomograms, parallel coordinate plots, activity-centered design. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190,&ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo; 
National Science Foundation: 
NSF-CNS-1625941, &ldquo;MRI - Development of Continuum: A Virtualized Attentive Environment for Amplified Collaboration&rdquo; 
NSF-DMS-1557559, &ldquo;QuBBD Collaborative Research SMART Spatial Nonspatial Multidimensional Adaptive Radiotherapy Treatment&rdquo;

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

Authors: Hesham Elhalawani, Timothy A Lin, Stefania Volpe, Abdallah S.R. Mohamed, Aubrey L. White, James Zafereo, Andrew Wong, Joel E. Berends, Shady Abohashem, Bowman Williams, Jeremy M. Aymard, Aasheesh Kanwar, Subha Perni, Crosby D. Rock, Luke Cooksey, Shauna Campbell, Pei Yang, Khanh Nguyen, Rachel Ger, Carlos Eduardo Cardenas, Xenia Fave, Carlo Sansone, Gabriele Piantadosi, Stefano Marrone, Rongjie Liu, Chao Huang, Kaixian Yu, Tenfei Li, Yang Yu, Youyi Zhang, Hongtu Zhu, Jeffrey S. Morris, Veerabhadran Baladandayuthapani, John W. Shumway, Alokananda Ghosh, Andrei Pöhlmann, Hady Ahmady Phoulady, Vibhas Goyal, Guadalupe Canahuate, G. Elisabeta Marai, David Vock, Stephen Y. Lai, Dennis S. Mackin, Laurence E. Court, John Freymann, Keyvan Farahani, Jayashree Kalpathy-Cramer, Clifton D Fuller 
Abstract: Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants
were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants’ feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyperdimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings. 
Keywords: machine learning, radiomics challenge, radiation oncology, head and neck, big data

Workflow of radiomics challenges.

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons from Open-Source Radiomics Challenges

SAGE2 is a user-centered platform for small groups or large distributed teams to access digital media datasets from various sources and juxtapose, share and investigate content on tiled display walls. SAGE2 builds on SAGE (Scalable Adaptive Graphics Environment), developed with prior NSF funding. With 90 SAGE2 sites worldwide since 2015, SAGE2 is having a transformative effect on data exploration, visualization and collaboration, making cyberinfrastructure more accessible to end users, both in the laboratory and in the classroom. 
<a href="https://figshare.com/articles/journal_contribution/SAGE2_Scalable_Amplified_Group_Environment/6174071/1">2018 Poster: https://figshare.com/articles/journal_contribution/SAGE2_Scalable_Amplified_Group_Environment/6174071/1</a> 
<a href="https://figshare.com/articles/presentation/SI2-SSI_SAGE2_Scalable_Amplified_Group_Environment_/6169574">2018: PPT: https://figshare.com/articles/presentation/SI2-SSI_SAGE2_Scalable_Amplified_Group_Environment_/6169574</a>

SAGE2 Scalable Amplified Group Environment (poster 2018)

SAGE2 is a web-based, user-centered platform for small groups or distributed teams to access digital media datasets from various
sources and share and investigate content on display walls. SAGE2 builds on SAGE, previously funded by NSF. By making cyberinfrastructure more accessible to end systems and to end users, both in the lab and in the classroom, SAGE2 is transforming data visualization, data exploration and collaboration. 
<a href="https://figshare.com/articles/presentation/SI2-SSI_SAGE2_Scalable_Amplified_Group_Environment_/6169574">2018: PPT: https://figshare.com/articles/presentation/SI2-SSI_SAGE2_Scalable_Amplified_Group_Environment_/6169574</a>

SI2-SSI: SAGE2 (Scalable Amplified Group Environment) (presentation 2018)

Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo;

A Composite Clinical/Serially-derived Parotid Gland Radiomics-based Model can be Correlated with Post-RT Xerostomia at 6 months in Oropharyngeal Cancer Patients Treated with Image-Guided Radiation Therapy

Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&ldquo;

A Combination of Serial Tumor Radiomics Features and key clinical attributes can predict the response of Head & Neck Cancers Treated with Image-Guided Radiation Therapy

In this paper, we present one saliency-based feature selection and tracking strategy in the feature-based stereoscopic panoramic video generation system. Many existing stereoscopic video composition approaches aim at producing high-quality panoramas from multiple input cameras; however, most of them directly operate image alignment on those originally detected features without any refinement or optimization. The standard global feature extraction threshold always fails to guarantee stitching correctness of all human interested regions. Thus, based on the originally commonly identified feature set, we incorporate the saliency map into the distribution of control points to remove the redundancy in texture-rich regions and ensure the adequacy of selected features in visual sensitive regions. Intuitively, under the guidance of saliency change in the video sequence, one grid-based feature updating strategy is operated between consecutive frames instead of the standard global feature updating. The experiments show that our method can improve the stitching quality of visual important region without impairment to the human less-interested regions in the generated stereoscopic panoramic video. 
Index Terms: Stereoscopic Panoramic Video, Commonly-identified Feature, Saliency Map, Visual Sensitive

Comparison of left view video stitching result between NFS and SFS.

Saliency-Based Feature Selection Strategy in Stereoscopic Panoramic Video Generation

Recent neuroscience research indicates that cognitive operations are performed not by individual brain regions working in isolation, but by networks that consist of several discrete brain regions. These discrete brain regions act in synchrony and share functional connectivity. The study of brain networks contains both non-spatial data dynamic functional connections within the brain and spatial data 2D/3D medical scans of the brain. To further understand the relationships between dynamic functional connectivities and physical structures of the brain, researchers analyze dynamic brain networks over multiple experiments or with different models. Similarly, bioinformatics research also contains problems about dynamic biological networks that require the integration of spatial and nonspatial data and multi-run comparison. Due to the complexity of biological data that can be collected, modeled and analyzed thanks to technological and algorithmic advancements, the difficulty in studying biological networks has increased over the last decades. Visualization provides an efficient way to help biologists understand, communicate, and gain insight into their biological data through visual exploration and analysis. 
In this dissertation, I propose a set of visual approaches for the analysis of ensemble dynamic biological networks. This set of approaches took shape through the development of multiple prototype visual analytics tools which were aimed to solve complex problems in multiple biological subdomains. The steering of the approach was supported through an in-depth understanding of both the encompassing biological domains and problem space, as well through a survey of existing visual approaches helping to generalize the paradigms used when confronting these problems and the complexity of their data.

RemBrain 2.0 implements a summary+detail visual approach for the analysis for comparative analysis of multiple brain networks at four scales: groups of subjects, individual subjects, activations of a subject, and ROIs of an activation.

Visual Analysis Techniques for Dynamic Biological Networks

UIC professor emeritus and EVL co-founder Daniel Sandin participates in a panel discussion on the merits of stereoscopic panoramic imaging at the Stereoscopic Displays and Applications XXIX Conference.  Other panelists include Greg Dawe (University of California San Diego) and Eric Kurland (3-D Space).

360° Imaging Should Be 3D - But Why And How? (panel presentation)

Radiomics is one such &ldquo;big data&rdquo; approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between &ldquo;favorable&rdquo; and &ldquo;unfavorable&rdquo; clusters were noted. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190,&ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo; 
National Science Foundation: 
NSF-DMS-1557559, &ldquo;QuBBD Collaborative Research SMART Spatial Nonspatial Multidimensional Adaptive Radiotherapy Treatment&rdquo;

Investigation of radiomics-based signature for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Although visualization design models exist in the literature in the form of higher-level methodological frameworks, these models do not present a clear methodological prescription for the domain characterization step. This work presents a framework and end-to-end model for requirements engineering in problem-driven visualization application design. The framework and model are based on the activity-centered design paradigm, which is an enhancement of human-centered design. The proposed activity-centered approach focuses on user tasks and activities, and allows an explicit link between the requirements engineering process with the abstraction stage-and its evaluation-of existing, higher-level visualization design models. In a departure from existing visualizationdesign models, the resulting model: assigns value to a visualization based on user activities; ranks user tasks before the user data; partitions requirements in activity-related capabilities and nonfunctional characteristics and constraints; and explicitly incorporates the user workflows into the requirements process. A further merit of this model is its explicit integration of functional specifications, a concept this work adapts from the software engineering literature, into the visualization design nested model. A quantitative evaluation using two sets of interdisciplinary projects supports the merits of the activity-centered model. The result is a practical roadmap to the domain characterization step of visualization design for problem-driven data visualization. Following this domain characterization model can help remove a number of pitfalls that have been identified multiple times in the visualization design literature. 
Index Terms - Design studies, Tasks and requirements analysis, Visualization models, Domain characterization, Activity-centered design, Functional specifications 
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